load_prediction.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import typing\n",
    "import seaborn as sns\n",
    "from matplotlib import pyplot as plt\n",
    "import tensorflow as tf\n",
    "import numpy as np\n",
    "import scipy.stats as stats\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>load</th>\n",
       "      <th>time</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>datetime</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2023-01-01 00:00:00</th>\n",
       "      <td>200</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-01-01 00:01:00</th>\n",
       "      <td>200</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-01-01 00:02:00</th>\n",
       "      <td>200</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-01-01 00:03:00</th>\n",
       "      <td>91</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-01-01 00:04:00</th>\n",
       "      <td>91</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-04-10 23:55:00</th>\n",
       "      <td>91</td>\n",
       "      <td>2395</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-04-10 23:56:00</th>\n",
       "      <td>91</td>\n",
       "      <td>2396</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-04-10 23:57:00</th>\n",
       "      <td>91</td>\n",
       "      <td>2397</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-04-10 23:58:00</th>\n",
       "      <td>91</td>\n",
       "      <td>2398</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-04-10 23:59:00</th>\n",
       "      <td>91</td>\n",
       "      <td>2399</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>144000 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                     load  time\n",
       "datetime                       \n",
       "2023-01-01 00:00:00   200     0\n",
       "2023-01-01 00:01:00   200     1\n",
       "2023-01-01 00:02:00   200     2\n",
       "2023-01-01 00:03:00    91     3\n",
       "2023-01-01 00:04:00    91     4\n",
       "...                   ...   ...\n",
       "2023-04-10 23:55:00    91  2395\n",
       "2023-04-10 23:56:00    91  2396\n",
       "2023-04-10 23:57:00    91  2397\n",
       "2023-04-10 23:58:00    91  2398\n",
       "2023-04-10 23:59:00    91  2399\n",
       "\n",
       "[144000 rows x 2 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('data/load_data/Electricity_Profile.csv', sep=\",\", header=None)\n",
    "df = df.rename(columns={0: 'load'})\n",
    "df['time'] = df.index%2400\n",
    "freq_minutes = 1\n",
    "n_rows = len(df)\n",
    "date_col = pd.date_range(start='2023-01-01', periods=n_rows, freq=f'{freq_minutes}min')\n",
    "df['datetime'] = date_col\n",
    "df.set_index('datetime', inplace=True)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x1000 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "corrMatrix = df.corr(numeric_only=True)\n",
    "fig, ax = plt.subplots(figsize=(12, 10))\n",
    "sns.heatmap(corrMatrix, annot= True, ax=ax, fmt='.3f')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the time series\n",
    "start = 1000\n",
    "end = 2000\n",
    "plt.plot( df.iloc[start:end].index, df['load'].iloc[start:end])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.feature_selection import SelectKBest\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from sklearn.linear_model import RidgeCV\n",
    "from sklearn.compose import ColumnTransformer\n",
    "from sklearn.model_selection import cross_validate\n",
    "from sklearn.model_selection import train_test_split, TimeSeriesSplit\n",
    "from sklearn.metrics import mean_absolute_error\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.pipeline import make_pipeline\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score, mean_absolute_percentage_error\n",
    "from sklearn.preprocessing import FunctionTransformer\n",
    "from sklearn.model_selection  import RandomizedSearchCV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "Y = df[\"load\"].to_numpy()\n",
    "# feature matrix\n",
    "X = df[\"time\"].to_numpy().reshape(-1,1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Random Forest Regressor Model Training MAE: 123.08858\n",
      "Random Forest Regressor Mode Test MAE: 129.28518\n",
      "Prediction accuracy for  99.39293063486141\n",
      "Other Test metrics: \n",
      "MAE: 129.28518063482335\n",
      "MSE: 78970.87858417637\n",
      "RMSE: 281.01757700218036\n",
      "R2 score: -0.06613986634931068\n",
      "\n",
      "\n",
      "Random Forest Regressor Model Training MAE: 122.70199\n",
      "Random Forest Regressor Mode Test MAE: 142.66442\n",
      "Prediction accuracy for  99.3419191373583\n",
      "Other Test metrics: \n",
      "MAE: 142.66441906404555\n",
      "MSE: 111257.70059637273\n",
      "RMSE: 333.55314508541625\n",
      "R2 score: 0.00034295877085543935\n",
      "\n",
      "\n",
      "Random Forest Regressor Model Training MAE: 130.75603\n",
      "Random Forest Regressor Mode Test MAE: 131.72154\n",
      "Prediction accuracy for  99.40527555354794\n",
      "Other Test metrics: \n",
      "MAE: 131.72154374420447\n",
      "MSE: 82632.18675748934\n",
      "RMSE: 287.458147836323\n",
      "R2 score: -0.04385011365310487\n",
      "\n",
      "\n",
      "Random Forest Regressor Model Training MAE: 128.07904\n",
      "Random Forest Regressor Mode Test MAE: 121.91602\n",
      "Prediction accuracy for  99.43973421976742\n",
      "Other Test metrics: \n",
      "MAE: 121.91602467159434\n",
      "MSE: 61229.76791931597\n",
      "RMSE: 247.44649506371266\n",
      "R2 score: -0.023051964838905148\n",
      "\n",
      "\n",
      "Random Forest Regressor Model Training MAE: 125.12886\n",
      "Random Forest Regressor Mode Test MAE: 120.80597\n",
      "Prediction accuracy for  99.45082606158574\n",
      "Other Test metrics: \n",
      "MAE: 120.80596690864071\n",
      "MSE: 62432.31819495364\n",
      "RMSE: 249.8645997234375\n",
      "R2 score: 0.01589247173544006\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "Y = df[\"load\"].to_numpy()\n",
    "# feature matrix\n",
    "X = df[\"time\"].to_numpy().reshape(-1,1)\n",
    "\n",
    "def create_forest_model():\n",
    "    forestModel = make_pipeline(\n",
    "        SelectKBest(k=\"all\"),\n",
    "        StandardScaler(),\n",
    "        RandomForestRegressor(\n",
    "            n_estimators=100,\n",
    "            max_depth=50,\n",
    "            random_state=77,\n",
    "            n_jobs=-1\n",
    "        )\n",
    "    )\n",
    "    return forestModel\n",
    "\n",
    "tscv = TimeSeriesSplit()\n",
    "all_splits = list(tscv.split(X, Y))\n",
    "for (train, test) in all_splits:\n",
    "    X_train, X_test = X[train], X[test]\n",
    "    Y_train, Y_test = Y[train], Y[test]\n",
    "\n",
    "    forestModel = create_forest_model()\n",
    "    y_train = Y_train\n",
    "    y_test = Y_test\n",
    "\n",
    "    forestModel.fit(X_train, y_train)\n",
    "    print('Random Forest Regressor Model Training MAE:', round(mean_absolute_error(y_train, forestModel.predict(X_train)),5))\n",
    "    y_pred = forestModel.predict(X_test)\n",
    "    print('Random Forest Regressor Mode Test MAE:', round(mean_absolute_error(y_test, forestModel.predict(X_test)),5))\n",
    "    mape = mean_absolute_percentage_error(y_pred, y_test)\n",
    "    print(\"Prediction accuracy for \", 100-np.mean(mape))\n",
    "    \n",
    "    print(\"Other Test metrics: \")\n",
    "    mae = mean_absolute_error(y_test, y_pred)\n",
    "    mse = mean_squared_error(y_test, y_pred)\n",
    "    rmse = np.sqrt(mse)\n",
    "    r2 = r2_score(y_test, y_pred)\n",
    "\n",
    "    print(\"MAE:\", mae)\n",
    "    print(\"MSE:\", mse)\n",
    "    print(\"RMSE:\", rmse)\n",
    "    print(\"R2 score:\", r2)\n",
    "    print(\"\\n\")\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Random Forest best Regressor Model  Training MAE: 124.3683\n",
      "Random Forest best Regressor Model  Test MAE: 120.0969\n",
      "Prediction accuracy for  99.45568913835208\n",
      "Other Test metrics: \n",
      "MAE: 120.09690264676861\n",
      "MSE: 62089.07675933148\n",
      "RMSE: 249.17679819624354\n",
      "R2 score: 0.021302914444828458\n",
      "\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "tscv = TimeSeriesSplit()\n",
    "all_splits = list(tscv.split(X, Y))\n",
    "(train, test) = all_splits[-1]\n",
    "X_train, X_test = X[train], X[test]\n",
    "Y_train, Y_test = Y[train], Y[test]\n",
    "\n",
    "\n",
    "forestModel = create_forest_model()\n",
    "y_train = Y_train\n",
    "y_test = Y_test\n",
    "\n",
    "params = {\n",
    "    'selectkbest__k':  range(1, X_train.shape[1] + 1),\n",
    "    'randomforestregressor__n_estimators': [50, 100, 150, 200],\n",
    "    'randomforestregressor__max_depth': [10, 20, 30, 40, 50, 60,70]\n",
    "}\n",
    "rs = RandomizedSearchCV(forestModel,\n",
    "                    param_distributions=params,\n",
    "                    n_iter=10,\n",
    "                    n_jobs=-1,\n",
    "                    cv=10\n",
    "                    )\n",
    "\n",
    "\n",
    "rs.fit(X_train, y_train)\n",
    "best_model = rs.best_estimator_\n",
    "\n",
    "print('Random Forest best Regressor Model  Training MAE:', round(mean_absolute_error(y_train, best_model.predict(X_train)),5))\n",
    "y_pred = best_model.predict(X_test)\n",
    "print('Random Forest best Regressor Model  Test MAE:', round(mean_absolute_error(y_test, best_model.predict(X_test)),5))\n",
    "mape = mean_absolute_percentage_error(y_pred, y_test)\n",
    "print(\"Prediction accuracy for \", 100-np.mean(mape))\n",
    "plt.figure()\n",
    "plt.scatter(df.index[test], y_test, alpha = 0.1, color='black')\n",
    "# Plot the data and label it according to the SVM\n",
    "plt.scatter(df.index[test], y_pred)\n",
    "\n",
    "print(\"Other Test metrics: \")\n",
    "mae = mean_absolute_error(y_test, y_pred)\n",
    "mse = mean_squared_error(y_test, y_pred)\n",
    "rmse = np.sqrt(mse)\n",
    "r2 = r2_score(y_test, y_pred)\n",
    "\n",
    "print(\"MAE:\", mae)\n",
    "print(\"MSE:\", mse)\n",
    "print(\"RMSE:\", rmse)\n",
    "print(\"R2 score:\", r2)\n",
    "print(\"\\n\")\n",
    "\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "its good at prediciting general trends but not"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fit_params = stats.norm.fit(df['load'])\n",
    "\n",
    "# Generate values from the fitted distribution\n",
    "x = stats.norm(*fit_params).rvs(len(df))\n",
    "\n",
    "# Plot the histogram of the data\n",
    "plt.hist(df['load'], bins=10, density=True, alpha=0.5, label='Data')\n",
    "\n",
    "# Plot the probability density function (PDF) of the fitted distribution\n",
    "plt.plot(sorted(df['load']), stats.norm.pdf(sorted(df['load']), *fit_params), 'r-', label='Fitted Distribution')\n",
    "\n",
    "# Add labels and a legend\n",
    "plt.xlabel('Load')\n",
    "plt.ylabel('Probability Density')\n",
    "plt.legend()\n",
    "\n",
    "# Show the plot\n",
    "plt.show()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "SVM regressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.svm import SVR\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn import preprocessing\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>load</th>\n",
       "      <th>time</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>datetime</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2023-01-01 00:00:00</th>\n",
       "      <td>200</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-01-01 00:30:00</th>\n",
       "      <td>91</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-01-01 01:00:00</th>\n",
       "      <td>200</td>\n",
       "      <td>60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-01-01 01:30:00</th>\n",
       "      <td>91</td>\n",
       "      <td>90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-01-01 02:00:00</th>\n",
       "      <td>200</td>\n",
       "      <td>120</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-04-10 21:30:00</th>\n",
       "      <td>361</td>\n",
       "      <td>2250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-04-10 22:00:00</th>\n",
       "      <td>2361</td>\n",
       "      <td>2280</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-04-10 22:30:00</th>\n",
       "      <td>91</td>\n",
       "      <td>2310</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-04-10 23:00:00</th>\n",
       "      <td>91</td>\n",
       "      <td>2340</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-04-10 23:30:00</th>\n",
       "      <td>91</td>\n",
       "      <td>2370</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4800 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                     load  time\n",
       "datetime                       \n",
       "2023-01-01 00:00:00   200     0\n",
       "2023-01-01 00:30:00    91    30\n",
       "2023-01-01 01:00:00   200    60\n",
       "2023-01-01 01:30:00    91    90\n",
       "2023-01-01 02:00:00   200   120\n",
       "...                   ...   ...\n",
       "2023-04-10 21:30:00   361  2250\n",
       "2023-04-10 22:00:00  2361  2280\n",
       "2023-04-10 22:30:00    91  2310\n",
       "2023-04-10 23:00:00    91  2340\n",
       "2023-04-10 23:30:00    91  2370\n",
       "\n",
       "[4800 rows x 2 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df[::30]\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2399 (4800, 2)\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "training_size = 4800//2-1\n",
    "print(training_size, df.shape)\n",
    "train_data = df.iloc[:training_size]\n",
    "val_data = df.iloc[training_size:training_size+training_size]\n",
    "plt.plot(train_data.index, train_data[['load']], label = 'train data')\n",
    "plt.plot(val_data.index, val_data[['load']], label='test data')\n",
    "plt.legend()\n",
    "plt.gcf().autofmt_xdate()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(2399, 1) (2399, 1)\n",
      "(2399, 1) (2399, 1)\n"
     ]
    }
   ],
   "source": [
    "train_features, train_labels  = train_data.drop(\"load\",axis=1), train_data[[\"load\"]]\n",
    "test_features, test_labels = val_data.drop(\"load\",axis=1), val_data[[\"load\"]]\n",
    "print(train_features.shape, train_labels.shape)\n",
    "print(test_features.shape, test_labels.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "d:\\mukul\\anaconda3\\envs\\SOML\\lib\\site-packages\\sklearn\\utils\\validation.py:1143: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Results for C=1:\n",
      "MAPE for max 0.5417787247742664\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x232a9270be0>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "X_train, X_test = train_features, test_features\n",
    "y_train, y_test = train_labels, test_labels\n",
    "C = 1\n",
    "clf = SVR(kernel='poly', C=C)\n",
    "# Train the SVM on the training data\n",
    "clf.fit(X_train, y_train)\n",
    "# Use the SVM on the test data\n",
    "y_pred = clf.predict(X_test)\n",
    "\n",
    "# Print the results for this C value\n",
    "print(f\"Results for C={C}:\")\n",
    "mape = mean_absolute_percentage_error(y_pred, y_test)\n",
    "print(\"MAPE for max\", np.mean(mape))\n",
    "# Plot the SVM\n",
    "plt.figure()\n",
    "plt.title(f\"SVM with linear kernel and C={C}\")\n",
    "# Plot the actual results to compare to (faded)\n",
    "plt.plot(val_data.index[:50], val_data[['load']][:50], label='test data', alpha=0.1)\n",
    "# Plot the data and label it according to the SVM\n",
    "plt.scatter(val_data.index[:50], y_pred[:50])"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "GMM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Generated Samples:\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "d:\\mukul\\anaconda3\\envs\\SOML\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=10.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x232e7b5a190>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.mixture import GaussianMixture\n",
    "\n",
    "# Train the GMM on the training data\n",
    "gmm = GaussianMixture(n_components=3, random_state=0)\n",
    "gmm.fit(X_train)\n",
    "\n",
    "# Generate new samples from the learned GMM\n",
    "n_samples = len(X_test)\n",
    "generated_samples = gmm.sample(n_samples)[0]\n",
    "\n",
    "# Print the generated samples\n",
    "print(\"Generated Samples:\")\n",
    "\n",
    "# Plot the generated samples\n",
    "plt.plot(val_data.index, val_data[['load']], label='test data', alpha=0.1)\n",
    "plt.scatter(val_data.index, generated_samples)\n",
    "plt.scatter()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "SOML",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.16"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}